Technical Field
Implementations and embodiments of the disclosure relate to particle filtering, especially used in the determination of movement of an image sensor or apparatus (e.g., a video camera) between successive video images (frames) captured by said apparatus, such as one incorporated in a platform, such as a digital tablet or a mobile cellular telephone for example, in particular a dynamic parameterization of such particle filter used for example in the estimation of ego-motion of said apparatus (i.e., the 3D motion of said apparatus in an environment (and accordingly the ego-motion of the platform incorporating said apparatus)), for example in a SLAM type algorithm.
Description of the Related Art
Particle filtering is widely used in many domains of computer vision, from tracking to odometry. Also known as sequential Monte-Carlo estimation, it is a density estimator that utilizes a sampling strategy to represent the posterior density of the state probability [see for example Lui, J. S. & Chen, R., 1998. Sequential Monte Carlo Methods for Dynamic Systems. Journal of the American Statistical Association, 93(443), pp. 1032-1044, incorporated by reference in the present patent application].
A conventional particle filter is very accurate but costs a lot of computational resources.